Rapid and Accurate Airfoil Aerodynamic Prediction Using a Multi-Fidelity Transfer Learning Approach
Year: 2025
Type: article
Abstract: The high computational cost of high-fidelity CFD simulations forms a major bottleneck in aerodynamic design. This paper introduces a multi-fidelity transfer learning framework to rapidly predict airfoil aerodynamics with high accuracy. Our approach involves pre-training a deep fully connected neural network on a large dataset of low-fidelity Euler simulations. The pre-trained model is then fine-tuned using a limited set of high-fidelity RANS data, enabling efficient knowledge transfer from low- to high-fidelity domains. A specialized logarithmic-exponential normalization method is developed to handle the scale differences between aerodynamic coefficients. The framework demonstrates exceptional performance: after fine-tuning with only 700 high-fidelity samples, the model accurately predicts pressure distributions (lowest RMSE = 0.053) and force coefficients (R2 > 0.947 for lift and drag). This method successfully bridges the gap between computational efficiency and high accuracy, providing a powerful data-driven surrogate model that can significantly accelerate the aerodynamic design and optimization process.
Source: Applied Sciences
DOI: 10.3390/app151910820
Authors: Yuxin Huo, Xue Che, Yiyu Wang, Qiang Jiang, Zhilong Zhong, Miao Zhang, Bo Wang, Xiaoping Ma
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4414989367